TP2 - Plénière 4 / Plenary 4

Inverse optimization is increasingly used to estimate unknown parameters in an optimization model based on decision data. However, when such “point estimates” are used prescribe downstream decisions, the resulting decisions may be of low-quality and misaligned with human intuition, and thus less likely to be adopted. To tackle this challenge, we propose a novel decision recommendation pipeline that learns an uncertainty set for the unknown parameters and then solves a robust optimization model to prescribe new decisions. We show that the suggested decisions can achieve bounded optimality gaps, as evaluated using both the ground-truth parameters and human perceptions. Our method demonstrates strong empirical performance compared to the standard inverse optimization pipeline. Finally, we perform a case study where we apply this new pipeline to provide delivery route recommendations in Toronto, Canada. Our approach achieves a significantly higher delivery path adherence rate than current industry practices without compromising service quality. Moreover, our method provides a better trade-off between absolute and perceived decision quality than baselines under various realistic scenarios, including cases with model mis-specification and data scarcity.